Título :	Classificação orientada a objeto em associação às ferramentas reflectância acumulada e mineração de dados
Otros títulos :	Object-oriented classification in association with accumulated reflectance and data mining tools
Autor :	Grande, Thallita Oliveira de Almeida, Tati de Cicerelli, Rejane Ennes
Assunto::	Análise baseada em objeto Análise multitemporal Antropização Mineração Sensoriamento remoto
Fecha de publicación :	dic-2016
Editorial :	Embrapa Informação Tecnológica - Pesquisa Agropecuária Brasileira
Citación :	GRANDE, Thallita Oliveira de; ALMEIDA, Tati de; CICERELLI, Rejane Ennes. Classificação orientada a objeto em associação às ferramentas reflectância acumulada e mineração de dados. Pesquisa Agropecuária Brasileira, Brasília, v. 51, n. 12, p. 1983-1991, dez. 2016. Disponível em: <http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2016001201983&lng=en&nrm=iso>. Acesso em: 13 mar. 2018. doi: http://dx.doi.org/10.1590/s0100-204x2016001200009.
Abstract:	The objective of this work was to use the accumulated reflectance technique and data mining application, followed by object-oriented classification, in images of Operational Land Imager (OLI) sensor, Landsat 8, for the classification of native vegetation and agricultural coverage of Cerrado. Four reflectance images were used for the discrimination of six classes - agriculture, livestock, wetland, savannah, forest, and grassland -, for classification of Parque Nacional das Emas and surrounding areas in the state of Goiás, Brazil. The images were segmented for the extraction of sample spectral attributes and application of attribute combinations (mean + mode, all attributes) on data mining. The Weka software was used to construct the decision trees. This methodology indicated that the differentiation among targets increased from the temporal accumulation of the reflectance in all bands and classes, and that the optimal image was that of the sum of the four dates. The classification based on the attribute associations mean + mode showed no restraints in the decision rules processing, unlike the association of all attributes. The mean + mode classification showed a satisfactory accuracy (global accuracy, 69%; Kappa, 58%; and TAU, 63%). The integration of these techniques shows potential to differentiate native and anthropogenic vegetation in the Cerrado.
Licença::	Pesquisa Agropecuária Brasileira - Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY 4.0). Fonte: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2016001201983&lng=en&nrm=iso. Acesso em: 13 mar. 2018.
DOI:	http://dx.doi.org/10.1590/s0100-204x2016001200009
Aparece en las colecciones:	Artigos publicados em periódicos e afins

Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.